Evidence map›Paper›PMID 41203783›Full record

ArticleNPJ precision oncology2025

Targeting tumoral heterogeneity in lung cancer: a novel, CT-texture-guided targeted biopsy approach with exome sequencing.

Alexander Hertel, Alexander Streuer, Steffen Diehl, Tobias Boch, Dominik Nörenberg, Anika Strittmatter, Frank G Zöllner, Stefan O Schoenberg, Wolf-Karsten Hofmann, Sonja Loges and 2 more

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In one paragraph

Article in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Alexander Hertel *Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany. alexander.hertel@umm.de.
Alexander Streuer *Department of Hematology and Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Steffen DiehlDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Tobias BochDepartment of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Dominik NörenbergDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Anika StrittmatterComputer Assisted Clinical medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Frank G ZöllnerComputer Assisted Clinical medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Stefan O SchoenbergDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Wolf-Karsten HofmannDepartment of Hematology and Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Sonja LogesDepartment of Personalized Oncology, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Daniel Nowak *Department of Hematology and Oncology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.
Matthias F Froelich *Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Solid tumors like lung cancer show significant mutational heterogeneity. A biopsy captures only focal aspects, limiting conclusions about overall tumor biology. This prospective study correlated CT-based radiomics features with genomic profiles to optimize biopsy site selection. Lung cancer patients underwent CT imaging, radiomics analysis, targeted biopsies, and whole-exome sequencing. Twelve non-redundant features were extracted, with JointEntropy guiding biopsy targeting. In 7 of 12 patients, over 10% of mutations were exclusive to one biopsy. Clonal reconstruction showed heterogeneous profiles with over two subclonal processes in 67% of cases. Unsupervised clustering of radiomics features revealed two distinct groups separated by entropy features, of which the entropy-rich cluster was associated with STK11 mutations. Our study demonstrates that integrating radiomics with localized genomic analysis enhances the understanding of tumoral heterogeneity and may improve the targeting of advanced tumor regions for diagnostic sampling.

Identifiers

PMID41203783
PMCPMC12594912

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.